Multidimensional scaling (MDS) refers to a set of algorithms that can be used to explore the intrinsic structure of a dataset. The sole input to MDS is a matrix consisting of distance measures between pairs of data points. These proximities are used to generate a spatial representation that can reveal hidden structure within the input data. We describe a novel application for MDS in estimating the spatial organization and dimensionality within fMRI datasets. Data were extracted from primary visual cortex following retinotopic mapping experiments that used both phase-encoded (wedge/ring) and non-phase-encoded (‘moving bar’) stimuli. The correlation distances between pairs of voxels were computed from the BOLD timeseries to generate the input for MDS. On the basis of these correlations, MDS was able to reconstruct retinotopic polar-angle and eccentricity maps that corresponded to the functional architecture of the original region of interest. Two dimensions in the spatial reconstruction satisfactorily account for the variance in the dataset, reflecting two primary organizational principles of neurons in V1. The quality of the MDS reconstruction is critically dependent on the quality of the input data, thus appropriate techniques for spatial and temporal filtering of the timeseries data were explored. It is suggested that extension of the described methods to fMRI resting state datasets will allow stimulus-free analysis of retinotopic maps throughout visual cortex. Additionally, MDS is appropriate for the objective assessment of changes in visual field maps in a variety of disease states.